Familiarity with Big Data, Privacy Concerns, and Self-Disclosure Accuracy in Social Networking Websites: An APCO Model

2017 ◽  
Vol 41 ◽  
pp. 62-96 ◽  
Author(s):  
Tawfiq Alashoor ◽  
Sehee Han ◽  
Rhoda C. Joseph
Keyword(s):  
Big Data ◽  
Social Networking ◽  
Data Privacy ◽  
Self Disclosure ◽  
Privacy Concerns ◽  
Social Networking Websites ◽  
Big Data Privacy

Driving for Big Data? Privacy Concerns in Vehicular Networking

2014 ◽  
Vol 12 (1) ◽  
pp. 77-79 ◽  
Author(s):  
David Eckhoff ◽  
Christoph Sommer
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy Concerns ◽  
Vehicular Networking ◽  
Big Data Privacy

Employing a Mixed Method to Explore Mobile Social Media Users' Big Data Privacy Concerns

2015 ◽  
pp. 302-320
Author(s):  
Kenneth C. C. Yang ◽  
Yowei Kang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Data Privacy ◽  
Mobile Technologies ◽  
Personal Privacy ◽  
Privacy Concerns ◽  
Early 21St Century ◽  
Data Practices ◽  
Networking Technologies

Since its introduction in the early 21st century, mobile social media have played an indispensable part in contemporary human experiences. The convergence of social networking and mobile technologies and services creates a fascinating circumstance because the pervasive nature of mobile social networking technologies has impacted on users' privacy. The chapter employed a mixed research method to collect and analyze mobile social media users' experiences and privacy concerns in the age of Big Data. A total of 57 participants were included in this study. Collected data was analyzed by examining mobile social media users' experiences and their concerns over privacy. Findings from this study showed the rising concerns over personal privacy as a result of convergence of mobile social media and Big Data practices by the advertising industry. Theoretical and practical implications were discussed.

Employing a Mixed Method to Explore Mobile Social Media Users' Big Data Privacy Concerns

2016 ◽  
pp. 1528-1548
Author(s):  
Kenneth C. C. Yang ◽  
Yowei Kang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Data Privacy ◽  
Mobile Technologies ◽  
Personal Privacy ◽  
Privacy Concerns ◽  
Early 21St Century ◽  
Data Practices ◽  
Networking Technologies

Since its introduction in the early 21st century, mobile social media have played an indispensable part in contemporary human experiences. The convergence of social networking and mobile technologies and services creates a fascinating circumstance because the pervasive nature of mobile social networking technologies has impacted on users' privacy. The chapter employed a mixed research method to collect and analyze mobile social media users' experiences and privacy concerns in the age of Big Data. A total of 57 participants were included in this study. Collected data was analyzed by examining mobile social media users' experiences and their concerns over privacy. Findings from this study showed the rising concerns over personal privacy as a result of convergence of mobile social media and Big Data practices by the advertising industry. Theoretical and practical implications were discussed.

Big Data Privacy Preservation Using Two Phase Top-Down Specialization Algorithm with Multidimensional Map Reduce Framework on Hadoop

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Shalin Eliabeth S. ◽  
Sarju S.
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Experimental Result ◽  
Map Reduce ◽  
Distributed Environment ◽  
Top Down ◽  
Two Phase ◽  
Data Anonymization ◽  
Big Data Privacy

Big data privacy preservation is one of the most disturbed issues in current industry. Sometimes the data privacy problems never identified when input data is published on cloud environment. Data privacy preservation in hadoop deals in hiding and publishing input dataset to the distributed environment. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, many cloud applications with big data anonymization faces the same kind of problems. For recovering this kind of problems, here introduced a data anonymization algorithm called Two Phase Top-Down Specialization (TPTDS) algorithm that is implemented in hadoop. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization in map reduce framework, here implemented proposed Two-Phase Top-Down Specialization anonymization algorithm in hadoop and it will increases the efficiency on the big data processing system. By conducting experiment in both one dimensional and multidimensional map reduce framework with Two Phase Top-Down Specialization algorithm on hadoop, the better result shown in multidimensional anonymization on input adult dataset. Data sets is generalized in a top-down manner and the better result was shown in multidimensional map reduce framework by the better IGPL values generated by the algorithm. The anonymization was performed with specialization operation on taxonomy tree. The experiment shows that the solutions improves the IGPL values, anonymity parameter and decreases the execution time of big data privacy preservation by compared to the existing algorithm. This experimental result will leads to great application to the distributed environment.

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